Using heart rate variability to develop a predictive model for post‐operative cardiovascular complications following major abdominal surgery: A pilot study

Abstract

AbstractBackgroundHeart Rate Variability (HRV) is a dynamic reflection of heart rhythm regulation by various physiological inputs. HRV deviations have been found to correlate with clinical outcomes in patients under physiological stresses. Perioperative cardiovascular complications occur in up to 5% of adult patients undergoing abdominal surgery and are associated with significantly increased mortality. This pilot study aimed to develop a predictive model for post‐operative cardiovascular complications using HRV parameters for early risk stratification and aid post‐operative clinical decision‐making.MethodsAdult patients admitted to High Dependency Units after elective major abdominal surgery were recruited. The primary composite outcome was defined as cardiovascular complications within 7 days post‐operatively. ECG monitoring for HRV parameters was conducted at three time points (pre‐operative, immediately post‐operative, and post‐operative day 1) and analyzed based on outcome group and time interactions. Candidate HRV predictors were included in a multivariable logistic regression analysis incorporating a stepwise selection algorithm.Results89 patients were included in the analysis, with 8 experiencing cardiovascular complications. Three HRV parameters, when measured immediately post‐operatively and composited with patient age, provided the basis for a predictive model with AUC of 0.980 (95% CI: 0.953, 1.00). The negative predictive value was 1.00 at a statistically optimal predicted probability cut‐off point of 0.16.ConclusionOur model holds potential for accelerating clinical decision‐making and aiding in patient triaging post‐operatively, using easily acquired HRV parameters. Risk stratification with our model may enable safe early step‐down care in patients assessed to have a low risk profile of post‐operative cardiovascular complications.